Unified Model Research Articles

Design and performance of a triaxial load cell for conical picks

Measuring the load on picks in mining operations poses a significant challenge, which hinders the optimization of cutting mechanism design. To address this issue, a unified load measurement model was initially developed using angular transformation. Subsequently, the measuring-point positions were determined based on the principles of large strain and ease of measurement, using Finite Element Analysis. By analyzing the strain law of the measuring-point positions, a sensor layout scheme was designed, which led to the development of a triaxial load cell. Utilizing the piecewise least squares method and BP neural network, the calibration and decoupling were performed. The findings indicated that the BP neural network effectively reduced the error. Finally, cutting experiments were conducted. The error was less than 8 %, effectively identifying the load variation characteristics when cutting materials with different hardness. The results demonstrate that the designed triaxial load cell can accurately and reliably capture cutting loads.

One Noise to Rule Them All: Learning a Unified Model of Spatially-Varying Noise Patterns

Procedural noise is a fundamental component of computer graphics pipelines, offering a flexible way to generate textures that exhibit "natural" random variation. Many different types of noise exist, each produced by a separate algorithm. In this paper, we present a single generative model which can learn to generate multiple types of noise as well as blend between them. In addition, it is capable of producing spatially-varying noise blends despite not having access to such data for training. These features are enabled by training a denoising diffusion model using a novel combination of data augmentation and network conditioning techniques. Like procedural noise generators, the model's behavior is controllable via interpretable parameters plus a source of randomness. We use our model to produce a variety of visually compelling noise textures. We also present an application of our model to improving inverse procedural material design; using our model in place of fixed-type noise nodes in a procedural material graph results in higher-fidelity material reconstructions without needing to know the type of noise in advance. Open-sourced materials can be found at https://armanmaesumi.github.io/onenoise/

A simple model for two-proton radioactivity* *Supported in part by the National Natural Science Foundation of China (12175100, 11975132), the Construct Program of the Key Discipline in Hunan Province, the Research Foundation of Education Bureau of Hunan Province, China (21B0402, 18A237), the Natural Science Foundation of Hunan Province, China (2018JJ2321), the Innovation Group of Nuclear and Particle Physics in USC,

In this work, considering the preformation factor of the emitted two protons in parent nucleus and the effect of the parent nucleus deformation, based on the Wentzel-Kramers-Brillouin approximation and Bohr-Sommerfeld quantization condition, we improve a simple phenomenological model proposed by Bayrak [J. Phys. G: 47, 025102 (2020)] to systematically study radioactivity half-lives. This model contains two adjustable parameters and , which are related to the depth of nuclear potential and effect of deformation. The calculated results show that this model can effectively reproduce the experimental data with a corresponding root-mean-square (RMS) standard deviation of σ = 0.683. For comparison, we include the Gamow-like model (GLM) proposed by Liu et al. [Chin. Phys. C 45, 044110 (2021)], generalized liquid drop model (GLDM) proposed by Cui et al. [Phys. Rev. C 101, 014301 (2020)], effective liquid drop model (ELDM) proposed by M. Gonalves et al. [Phys. Lett. B 774, 14 (2017)], two-potential approach with Skyrme-Hartree-Fock (TPASHF) proposed by Pan et al. [Chin. Phys. C 45, 124104 (2021)], phenomenological model with a screened electrostatic barrier (SEB) propoesed by Zou et al. [Chin. Phys. C 45, 104101 (2021)], unified fission model (UFM) proposed by Xing et al. [Chin. Phys. C 45, 124105 (2021)], Coulomb and proximity potential model for deformed nuclei (CPPMDN) proposed by Santhosh [Phys. Rev. C 104, 064613 (2021)], two-parameter empirical formula proposed by Liu et al. [Chin. Phys. C 45, 024108 (2021)], and four-parameter empirical formula proposed by Sreeja et al. [Eur. Phys. J. A 55, 33 (2019)]. In addition, we use this model to predict the radioactive half-lives of some possible potential nuclei whose radioactivity are energetically allowed or observed but not yet quantified in NUBASE2020.

Stress from Digital Work: Toward a Unified View of Digital Hindrance Stressors

Digital technologies, although enhancing productivity and communication, also contribute to technostress at work. This study addresses the fragmentation in existing models of hindrance technostressors by proposing a unified hierarchical model of digital hindrance stressors tailored to contemporary digital work environments. The research synthesizes various existing models and uses a mixed-methods approach, including a qualitative prestudy and extensive surveys with more than 5,800 participants to identify and validate 12 first-order and 5 second-order digital hindrance stressors. The new model offers both detailed and streamlined measurement tools, enhancing its applicability in diverse organizational contexts. For practitioners and policymakers, this study provides a comprehensive framework to assess and mitigate the adverse impacts of digital stressors. The unified model allows organizations to understand specific stressors their employees face and implement targeted interventions to improve well-being and productivity. By using this model, occupational health professionals can better address the psychological and physical health implications of technostress. Moreover, the findings offer actionable insights for designing digital work environments that minimize stress and foster a healthier, more productive workforce. This research bridges the gap between theoretical technostress models and practical applications, guiding effective strategies for managing digital workplace stress.

Characteristics and considerations of French medical education

Abstract French medical education, with a long history and guided by laws and decrees set by the French Ministry of Education and Ministry of Health, aims at elite training, focusing on the cultivation of students’ clinical practice and scientific research capabilities, and implementing a unified medical talent training model. The French medical education system continuously seeks innovation and change. In September 2020, a new round of reforms was initiated in aspects such as admission pathways, examination system, and assessment methods. These changes aim to enhance medical students’ disciplinary backgrounds, strengthen process-based assessment, and foster the development of versatile medical professionals. This article analyzes the characteristics of French medical education and the main measures of this round of reforms, providing insights and considerations for the training of cultivated medical talents in China.

Laser-induced breakdown spectroscopy as a method for millimeter-scale inspection of surface flatness

A non-contact method for millimeter-scale inspection of material surface flatness via Laser-Induced Breakdown Spectroscopy (LIBS) is investigated experimentally. The experiment is performed using a planished surface of an alloy steel sample to simulate its various flatness, ranging from 0 to 4.4 mm, by adjusting the laser focal plane to the surface distance with a step length of 0.2 mm. It is found that LIBS measurements are successful in inspecting the flatness differences among these simulated cases, implying that the method investigated here is feasible. It is also found that, for achieving the inspection of surface flatness within such a wide range, when univariate analysis is applied, a piecewise calibration model must be constructed. This is due to the complex dependence of plasma formation conditions on the surface flatness, which inevitably complicates the inspection procedure. To solve the problem, a multivariate analysis with the help of Back-Propagation Neural Network (BPNN) algorithms is applied to further construct the calibration model. By detailed analysis of the model performance, we demonstrate that a unified calibration model can be well established based on BPNN algorithms for unambiguous millimeter-scale range inspection of surface flatness with a resolution of about 0.2 mm.

Modeling for plastic instability in multi-directional rotary forging of thin-walled components with three ribs

Multi-directional rotary forging has the great potential to form thin-walled components with three ribs because of its incremental deformation characteristic and smaller forging load. However, for the components with thin-walled sterna and thick ribs, the plastic instability is easier to occur at the bottom of thick ribs in multi-directional rotary forging, resulting in the waste of components. Therefore, the aim of this paper is to establish the model for predicting plastic instability in multi-directional rotary forging of thin-walled components with three ribs. Firstly, the models for calculating actual and critical radial stresses on both sides of rib are established by analyzing complicated stress states caused by time-varying thinning deformation of sterna and thickening deformation of rib. It is found that the critical radial stress is mainly determined by ratio of deformation energy and external force work, while the actual radial stress is mainly determined by dissipated power and metal flow velocity. By combining actual and critical radial stresses, a unified model for simultaneously predicting plastic instability of ribs at different positions is proposed. Then, the influence laws of different technical parameters on critical conditions of plastic instability are revealed. It is found that the plastic instability is mainly determined by initial thickness of workpiece and width of rib, i.e., the critical deformation amount increases with increasing initial thickness of workpiece or decreasing width of rib, and vice versa. Finally, the experiments are conducted to verify that the proposed model for predicting plastic instability in multi-directional rotary forging of thin-walled components with three ribs is reliable. This paper provides a guideline to establish the model for predicting plastic instability under complicated stress states and metal flow modes.

A unified stochastic SIR model driven by Lévy noise with time-dependency

We propose a unified stochastic SIR model driven by Lévy noise. The model is structural enough to allow for time-dependency, nonlinearity, discontinuity, demography, and environmental disturbances. We present concise results on the existence and uniqueness of positive global solutions and investigate the extinction and persistence of the novel model. Examples and simulations are provided to illustrate the main results.

A Unified Adsorption Kinetic Model Inspired by Epidemiological Model: Based on Adsorbates "Infect" Adsorbents.

Adsorption is a unit operation used in various fields, including the environmental, chemical, and pharmaceutical industries. Understanding the adsorption kinetics is crucial to designing efficient adsorption systems. However, existing empirical adsorption models are limited in providing insights into the mass transfer mechanisms. Additionally, the absence of a unified adsorption kinetic model hampers the effective comparison of different adsorption systems. Here, we viewed the adsorption as an "infectious process of adsorbates by adsorbents" akin to epidemiology. In epidemiology, individuals can be divided into susceptible, infected, and recovered compartments, ignoring the complexities of movement among individuals. Analogously, we have categorized the adsorbates as adsorbable, adsorbed, and removed compartments. Thus, we proposed a unified adsorption kinetic model (the monolayer-multilayer-adsorbable-adsorbed-removed model) that accommodates monolayer/multilayer adsorption. The model was designed to encompass diverse adsorption setups, including continuous and batch processes with fixed/dispersed adsorbents. The versatility and applicability of the model were demonstrated through validation using a diverse set of experimental data. This validation underscored its effectiveness in water/wastewater treatment, salt reduction, metal recovery, and drug purification. A MATLAB-based program for solving this model was made available to researchers for their utilization and further investigations. Overall, this study developed a versatile adsorption kinetic model that offers a comprehensive and unified understanding of adsorption kinetics across various applications.

A unified model for mechanical deformations of single stranded DNA-microbeam biosensors considering surface charge effects

A unified energy model for mechanical deformation of the microbeam included by single stranded DNA (ssDNA) adsorption considering the surface charge density on substrate is investigated. First, a free energy model is established to quantify the deflection and axial displacement of the microbeam, the ion concentration in the solution, as well as the electric potential distribution inside and outside the ssDNA film with a uniform surface charge density. Then, the governing equations and corresponding boundary conditions are obtained by minimizing the free energy with three types of variational variables. And, the relationship of the electric potential difference between the two sides of the ssDNA film and the deformation of microcantilever and simply support beam are determined in different solutions (1: 1, 1: 2, 1: 3). Moreover, the numerical solutions of the electric potential distribution inside and outside the ssDNA film are investigated by using the finite difference method and Newton's iteration method. The results suggest that the nonlinear model for electric potential distribution should be used under higher surface charge density. By fitting and comparing the present predictions with Arroyo-Hernandez's experimental data, the effectiveness of the model is verified. The study shows that the surface charge density can modulate the magnitude and direction of ssDNA-microbeam deformation, which also indicates that there exists a critical surface charge density, causing the failure of ssDNA-microbeam detection. These conclusions are helpful for designing the efficient biosensors based on DNA-microbeam.

A Unified Extrapolation thermodynamic model for multicomponent solutions based on binary data

Predicting the thermodynamic properties of multicomponent solution based on binary data is highly desirable. However, traditional methods face many challenges in practical applications due to the unclear mechanisms for obtaining the molar composition of sub-binary terms. In this article, a new extrapolation model is suggested, which derived from the assumption of the Kohler model. It determines the molar composition points of each sub-binary system through a clear mechanism by introducing the contribution coefficient, defined by the property differences between two components. Moreover, the new model can mathematically obtain all potential molar composition points of sub-binary systems. Additionally, a simple and effective method for calculating the property difference between two components is recommended. The performance of this new extrapolation model is demonstrated in several multicomponent alloy systems with different properties.

The Met Office Forecast Ocean Assimilation Model (FOAM) using a 1/12‐degree grid for global forecasts

AbstractThe Met Office Forecast Ocean Assimilation Model (FOAM) ocean–sea‐ice analysis and forecasting operational system has been using an ORCA tripolar grid with 1/4° horizontal grid spacing since December 2008. Surface boundary forcing is provided by numerical weather prediction fields from the operational global atmosphere Met Office Unified Model. We present results from a 2‐year simulation using a 1/12° global ocean–sea‐ice model configuration while keeping a 1/4° data assimilation (DA) set‐up. We also describe recent operational data assimilation enhancements that are included in our 1/4° control and 1/12° simulations: a new bias‐correction term for sea‐level anomaly assimilation and a revised pressure correction algorithm. The primary effect of the first is to decrease the mean and variability of sea‐level anomaly increments at high latitudes, whereas the second significantly reduces the vertical velocity standard deviation in the tropical Pacific. The level of improvement achieved with the higher resolution configuration is moderate but consistently satisfactory when measured using neighbourhood verification metrics that provide fairer quantitative comparisons between gridded model fields at different spatial resolutions than traditional root‐mean‐square metrics. A comparison of the eddy kinetic energy from each configuration and an observation‐based product highlights the regions where further system developments are most needed.

Bioenergetic stress potentiates antimicrobial resistance and persistence.

Antimicrobial resistance (AMR) is a global health crisis and there is an urgent need to better understand AMR mechanisms. Antibiotic treatment alters several aspects of bacterial physiology, including increased ATP utilization, carbon metabolism, and reactive oxygen species (ROS) formation. However, how the "bioenergetic stress" induced by increased ATP utilization affects treatment outcomes is unknown. Here we utilized a synthetic biology approach to study the direct effects of bioenergetic stress on antibiotic efficacy. We engineered a genetic system that constitutively hydrolyzes ATP or NADH in Escherichia coli. We found that bioenergetic stress potentiates AMR evolution via enhanced ROS production, mutagenic break repair, and transcription-coupled repair. We also find that bioenergetic stress potentiates antimicrobial persistence via potentiated stringent response activation. We propose a unifying model that antibiotic-induced antimicrobial resistance and persistence is caused by antibiotic-induced. This has important implications for preventing or curbing the spread of AMR infections.

Lattice Boltzmann method for the linear complementarity problem arising from American option pricing

In this paper, a lattice Boltzmann method is proposed for solving the linear complementarity problem (LCP) arising in single and multi-asset American put option pricing. The LCP for American option is a variable coefficient parabolic model defined on an unbounded domain. Initially, using the far field estimate method and the penalty method respectively, the LCP could be reformulated into a nonlinear parabolic partial differential equation on a bounded domain. To construct a unified lattice Boltzmann model for the option pricing problems, the above transformation equations are rewritten into an equivalent divergence form. Then, through the incorporation of an amending function into the evolution equation, which assists in recovering the source term and eliminating the error term, the lattice Boltzmann model with spatial second-order accuracy is constructed. Finally, the present model is validated using numerical simulations, and the numerical results agree well with the option values obtained by existing methods, which indicates that the present lattice Boltzmann model is efficient for solving the American put option pricing problem.

Establishing a unified viscoplastic constitutive equation for EA4T steel: Comparative analysis with Arrhenius model

Quasi-static and impact compression experiments were conducted on EA4T steel using a Gleeble-3800 thermal simulation machine. The study aimed to investigate the complex microstructural evolution and thermal deformation behavior of EA4T steel under various experimental conditions, including temperatures ranging from 970 °C to 1170 °C and strain rates ranging from 0.01s−1 to 1s−1. To precisely elucidate these phenomena, we meticulously constructed a unified visco-plastic constitutive model using the internal variable methodology. The model's parameterization was achieved through the effective application of genetic algorithm optimization techniques. Rigorous validation of the model was performed by meticulously comparing its outputs with experimental data, including key metrics such as average grain size, recrystallized fraction, and effective flow stress. In addition,a comparative analysis with the improved Arrhenius model highlights the superior performance of the unified visco-plastic constitutive equation in capturing the intricate microstructural evolution and thermal deformation behavior exhibited by EA4T steel.

Study on a concise and unified unstable creep model for rocks

Study on a concise and unified unstable creep model for rocks

Ternary fission of various Z=120 isotopes with 3H, 4He and 8Be as light charge particle

The spontaneous cold ternary fission of various Z = 120 isotopes accompanied by light charge particles (LCP's)3H, 4He and 8Be in equatorial configuration is investigated using Unified ternary fission model (UTFM). The favourable ternary fission channel is predicted based on the computation of Q value, driving potential, barrier penetrability and relative yield of all potential ternary fragment configurations. For the parent isotopes 302,306,308,120 with LCP 3H, the maximum relative yield is for the combinations that have one fragment respectively as 131,132I and 139Ba, which are nearly magic. However, in the case of310120 with LCP 3H, the combination 145La+162Sm+3H exhibits the maximum relative yield. For the 4He accompanied fission of 302,306,308,310,120 isotopes, the fragmentation that include 139Cs, 140,144Ba and 131Te isotopes, which is in the vicinity of shell closure possess high relative yield. In the case of8Be accompanied ternary breakup of the parent isotopes 302,306,120, the maximum yield is due to the magic neutron shell and near-magic proton shell of 134Te. Whereas, for parent isotopes 308,310,120, it is due to the presence of respective doubly magic 132Sn and near doubly magic 131Sn as one fragment. Therefore, our findings emphasize the prominent role of both high Q value as well as existence of doubly and near doubly magic nuclei in the ternary fission of super heavy even-even isotopes 302,306–310,120, accompanied by light charge particles 3H, 4He and 8Be.

Singapore's COVID-19 crisis decision-making through centralization, legitimacy, and agility: an empirical analysis

Decision-making during health crises differs from routine decision-making and is constrained by ambiguity about evolving epidemiological situations, urgency of response, lack of evidence, and fear. Recent analyses of governance and decision-making during COVID-19, focusing on leadership qualities, involvement of specific stakeholders, and effective resource management, do not adequately address a persisting gap in understanding the determinants of decision-making during health crises at the national level. We undertook a study to understand the processes and characteristics of decision-making during the COVID-19 pandemic in Singapore. We used a case study approach and collected empirical evidence about public health decision-making, using a combination of key informant interviews and focus group discussions with stakeholders from government, academia and civil society organizations. We argue that administrative centralization and political legitimacy played important roles in agile governance and decision-making during the pandemic in Singapore. We demonstrate the role of the Singapore government's centralization in creating a unified and coherent governance model for emergency response and the People's Action Party's (PAP) legitimacy in facilitating people's trust in the government. Health system resilience and financial reserves further facilitated an agile response, yet community participation and prioritization of vulnerable migrant populations were insufficient in the governance processes. Our analysis contributes to the theory and practice of crisis decision-making by highlighting the role of political and administrative determinants in agile crisis decision-making. This study is funded by the U.S. Centers for Disease Control and Prevention through a Cooperative Research Agreement (NU2HGH2020000037).

Gender promotion gaps across business units in a multiunit organization: Supply‐ and demand‐side drivers

AbstractDrawing on gender role and gender queuing theories, we employ a multi‐stage process model to investigate demand‐ and supply‐side drivers of gender promotion gaps and to explore variations in these gaps across different business units within an organization. Analyzing 9 years of personnel records from a multiunit European bank, we find that the gender promotion gap is influenced by both supply‐side and demand‐side factors. Specifically, women are less likely than men to express a motivation to change to a new job or move to a different unit within the bank. Those who do express such motivation are as likely as men to be reassigned to new roles, but their moves are less likely to constitute promotions than are men's moves. Furthermore, gender promotion gaps vary significantly within the organization itself. Business units with the most significant gaps are in regions that have fewer available organizational positions to move into, diminishing women's motivation to seek such moves, and have jobs with numerous incumbents, decreasing women's chances to get a new job or secure a promotion upon doing so. This study extends gender role theory by creating a unified theoretical model that incorporates both employee and employer gender role perceptions as drivers of promotions. It contributes to gender queuing theory by demonstrating the theory's relevance to promotion outcomes.

Unveiling Hidden Insights in Gas Chromatography Data Analysis with Generative Adversarial Networks

The gas chromatography analysis method for chemical substances enables accurate analysis to precisely distinguish the components of a mixture. This paper presents a technique for augmenting time-series data of chemicals measured by gas chromatography instruments with artificial intelligence techniques such as generative adversarial networks (GAN). We propose a novel GAN algorithm called GCGAN for gas chromatography data, a unified model of autoencoder (AE) and GAN for effective time-series data learning with an attention mechanism. The proposed GCGAN utilizes AE to learn a limited number of data more effectively. We also build a layer of high-performance generative adversarial neural networks based on the analysis of the features of data measured by gas chromatography instruments. Then, based on the proposed learning, we synthesize the features embedded in the gas chromatography data into a feature distribution that extracts the temporal variability. GCGAN synthesizes the features embedded in the gas chromatography data into a feature distribution that extracts the temporal variability of the data over time. We have fully implemented the proposed GCGAN and experimentally verified that the data augmented by the GCGAN have the characteristic properties of the original gas chromatography data. The augmented data demonstrate high quality with the Pearson correlation coefficient, Spearman correlation coefficient, and cosine similarity all exceeding 0.9, significantly enhancing the performance of AI classification models by 40%. This research can be effectively applied to various small dataset domains other than gas chromatography data, where data samples are limited and difficult to obtain.